split-pdf

Splitting PDFs into structured notes uses adaptive chunking based on paper length and reads content in controlled batches with pause-for-confirmation. It generates an incremental notes.md file containing structured extraction tailored for academic papers.

Adaptive chunking divides academic PDFs into optimally-sized segments based on total paper length. The Python backend manages this batch reading process, pausing for confirmation between chunks to maintain accurate structured extraction.

Yes, you can use local PDF files for structured extraction by providing a local file path. The skill preserves original documents in an articles directory while generating per-batch structured notes in a separate notes.md file.

The best way to manage batch reading for long research papers is using a batch-confirm workflow that pauses between chunks. This approach applies adaptive chunking to divide the PDF and incrementally appends structured extraction notes to a notes.md file.

Yes, a Python environment is required because the skill uses a Python backend like PyPDF2 to read PDFs and manage chunking. This backend processes the local file input or search-derived papers and writes the structured extraction notes.

A limitation of automated PDF chunking is the required batch-confirm workflow, which pauses the process for user input between chunks. Additionally, the structured extraction notes are specifically tailored for empirical finance papers, which may limit applicability to other domains.